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from dataclasses import dataclass, field |
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from functools import total_ordering |
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from typing import Any |
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import numpy as np |
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from dataclasses_json import DataClassJsonMixin |
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@dataclass |
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@total_ordering |
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class MetricValue(DataClassJsonMixin): |
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""" |
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Represents the value of a metric to be optimized, which can be compared to other metric values. |
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Comparisons (and max, min) are based on which value is better, not which is larger. |
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""" |
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value: float | int | np.number | np.floating | np.ndarray | None |
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maximize: bool | None = field(default=None, kw_only=True) |
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def __post_init__(self): |
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if self.value is not None: |
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assert isinstance(self.value, (float, int, np.number, np.floating)) |
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self.value = float(self.value) |
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def __gt__(self, other) -> bool: |
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"""True if self is a _better_ (not necessarily larger) metric value than other""" |
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if self.value is None: |
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return False |
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if other.value is None: |
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return True |
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assert type(self) is type(other) and (self.maximize == other.maximize) |
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if self.value == other.value: |
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return False |
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comp = self.value > other.value |
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return comp if self.maximize else not comp |
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def __eq__(self, other: Any) -> bool: |
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return self.value == other.value |
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def __repr__(self) -> str: |
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return str(self) |
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def __str__(self) -> str: |
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if self.maximize is None: |
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opt_dir = "?" |
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elif self.maximize: |
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opt_dir = "↑" |
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else: |
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opt_dir = "↓" |
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return f"Metric{opt_dir}({self.value_npsafe:.4f})" |
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@property |
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def is_worst(self): |
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"""True if the metric value is the worst possible value.""" |
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return self.value is None |
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@property |
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def value_npsafe(self): |
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return self.value if self.value is not None else float("nan") |
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@dataclass |
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class WorstMetricValue(MetricValue): |
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""" |
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Represents an invalid metric value, e.g. when the agent creates a buggy solution. |
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Always compares worse than any valid metric value. |
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""" |
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value: None = None |
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def __repr__(self): |
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return super().__repr__() |
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def __str__(self): |
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return super().__str__() |
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